Determinação de regimes de escoamento gás-líquido em leito fixo utilizando redes neurais artificiais

• Ano de defesa: 2012
• Autor(a) principal: Zeni, Lucas Maycon Hoff
• Orientador(a): Palú, Fernando
• Banca de defesa: Borba, Carlos Eduardo , Faria, Sérgio Henrique Bernardo de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual do Oeste do Parana
• Programa de Pós-Graduação: Programa de Pós-Graduação Stricto Sensu em Engenharia Química
• Departamento: Desenvolvimento de Processos
• País: BR
• Palavras-chave em Português: Redes neurais artificiais; Regimes de escoamento; Leito fixo; Escoamento bifásico; Redes neurais (Computação); Inteligência artificial - Aplicações industriais; Gás-liquido; Processos industriais - Modelagem; Calor - Transmissão; Transferência de massa
• Palavras-chave em Inglês: Artificial neural networks; Flow regimes; Fixed bed; Biphasic flow
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA QUIMICA::PROCESSOS INDUSTRIAIS DE ENGENHARIA QUIMICA
• Link de acesso: http://tede.unioeste.br:8080/tede/handle/tede/1834

Configuration of fixed bed that operates with biphasic flow is used in industrial operations such as the Fischer-Tropsch, hydrogenation, and residual water treatments. Vital information for the project and operation of this type of bed is in its characteristics fluid-dynamic and among these characteristics the flow regime because these have a direct influence transferring heat and mass present in the bed. In the two-phase flow with ascendant flow through fixed bed, three distinct regimes can be identified: the bubble regime, for low gas flow; pulsating regime, for moderate liquid and gas flow; and spray regime; for low flow of liquid and high flow rates of gas. Although there are different techniques to determine flow regimes, the most used is the visual identification. Thus, this research aims to develop, by using artificial neural networks (ANNs) a way to determine, for a given set of liquid-gas flow what out-flow regime the bed presents. To do so, firstly, the out-flow regime were identified by using water and air, respectively flux mass flowing varying from 2 to 16.5 kg.m-2.s-1 and from 0 to 0.6 kg.m-2.s-1, flowing up-words through a fixed bed packed with glass spheres measuring from 2.7 to 3.5 mm of diameter. The network proposed to identify the regimes contains Multiple Layers Perceptron architecture (PML) trained by the back propagation algorithm put together by applying the Multiple Back-Propagation (MBP) software, version 2.2.3 consistently with two input neurons, two intermediate layers, and four output neurons. The number of neurons of the intermediate layers was assorted to find out the best configuration. As activation of function, logistic, tangent, hyperbolic, and Gaussian were tested. Observed results showed that it is possible the identification of regimes through neural networks and among those tested the one that showed the best performance was the one that used the hyperbolic-tangent activation function; 10 neurons in the first hidden layer, and 12 neurons in the second hidden layer.